SmartImpute is a targeted imputation framework for single-cell transcriptome data

In this study, we present SmartImpute, a targeted imputation framework for single-cell RNA sequencing (scRNA-seq) data. SmartImpute focuses on predefined marker genes to improve biological relevance and computational efficiency. It employs a modified generative adversarial imputation network (GAIN) with a multi-task discriminator that imputes missing values while preserving true biological zeros. We applied SmartImpute to scRNA-seq datasets from head and neck squamous cell carcinoma, human bone marrow, and lung cancer. Across these datasets, SmartImpute improved clustering, cell type annotation, and trajectory inference and successfully scaled to datasets with over one million cells. In addition, SmartImpute was applied to spatial transcriptomics data, where it improved spatial gene expression patterns and clustering. These results demonstrate that SmartImpute facilitates deeper insights into cellular heterogeneity and disease progression.